My main research goal is to develop information processing and machine learning methods for cyber-physical and social systems. I work on machine learning, Internet of Things (IoT), semantic web, adaptive algorithms, stream processing and information search and retrieval to solve problems and develop new technologies for the future Internet/Web and healthcare systems. My colleagues and I are currently working on:

AI and IoT enabled solutions to provide personalised care for people affected by Dementia

The Internet is expanding to reach the real world, integrating the physical world into the digital world in what is called the Real World Internet (RWI). Sensor and actuator networks deployed all over the Internet will play the role of collecting sensor data and context information from the physical world and integrating it into the future RWI. In this paper we present the SENSEI architecture approach for the RWI; a layered architecture composed of one or several context frameworks on top of a sensor framework, which allows the collection of sensor data as well as context information from the real world. We focus our discussion on how the modeling of information is done for different levels (sensor and context data), present a multi-layered information model, its representation and the mapping between its layers.

The Internet extends its reach to the real world through innovations collectively termed the Internet of Things (IoT). The IoT aims at integrating technologies such as radio frequency identification, wireless sensor and actuator networks (WSANs), and networked embedded devices. Recent ideas envision the Internet as an all encompassing infrastructure that connects the physical into the digital world: the real world Internet (RWI). The European project SENSEI plays a leading role within the current efforts to create an underlying architecture and services for the future Internet and to realize the vision of the RWI.

Multimedia data are illusory entities for the machines. Their contents include interpretable data as well as binary representations. Understanding and accessing the content-driven information for multimedia objects allow us to design an efficient multimedia querying and retrieval system. In this paper, we propose a framework to represent the multimedia information and object roles in order to generate automatic multimedia presentations. The proposed architecture attempts to represent the semantic information and the relations amongst the multimedia objects in a disclosure domain. Thus, the system is domain dependent. The represented data associates with the presentation mechanisms to create an integrated presentation generation system. A multi-layer design defines the various levels of abstraction for the proposed framework.

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k-means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.

This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes.

Developments in (wireless) sensor and actuator networks and the capabilities to manufacture low cost and energy efficient networked embedded devices have lead to considerable interest in adding real world sense to the Internet and the Web. Recent work has raised the idea towards combining the Internet of Things (i.e. real world resources) with semantic Web technologies to design future service and applications for the Web. In this paper we focus on the current developments and discussions on designing Semantic Sensor Web, particularly, we advocate the idea of semantic annotation with the existing authoritative data published on the semantic Web. Through illustrative examples, we demonstrate how rule-based reasoning can be performed over the sensor observation and measurement data and linked data to derive additional or approximate knowledge. Furthermore, we discuss the association between sensor data, the semantic Web, and the social Web which enable construction of context-aware applications and services, and contribute to construction of a networked knowledge framework.

Knowledge Acquisition Toolkit (KAT) is an open-source software that includes methods to process numerical sensory data. KAT is able to extract and represent human understandable and/or machine interpretable information from raw data.

KAT includes a collection of algorithms for each step of the Internet of Things (IoT) data processing workflow ranging from data and signal pre-processing algorithms such as Frequency Filters, dimensionality reduction techniques such as Wavelet, FFT, SAX, and Feature Extraction and Abstraction and Inference methods such as Clustering. Figure 1 shows the steps of the process chain for processing cyber-physical data on the Web. KAT can be used to design and evaluate algorithms for sensor data that aim to extract and find new insights from the data.

The wide field of wireless sensor networks requires that hun-
dreds or even thousands of sensor nodes have to be main-
tained and configured. With the upcoming initatives such
as Smart Home and Internet of Things, we need new mecha-
nism to discover and manage this amount of sensors. In this
paper, we describe a middleware architecture that uses con-
text information of sensors to supply a plug-and-play gate-
way and resource management framework for heterogeneous
sensor networks. Our main goals are to minimise the effort
for network engineers to configure and maintain the network
and supply a unified interface to access the underlying het-
erogeneous network. Based on the context information such
as battery status, routing information, location and radio
signal strength the gateway will configure and maintain the
sensor network. The sensors are associated to nearby base
stations using an approach that is adapted from the 802.11
WLAN association and negotiation mechanism to provide
registration and connectivity services for the underlying sen-
sor devices. This abstracted connection layer can be used to
integrate the underlying sensor networks into high-level ser-
vices and applications such as IP-based networks and Web
services.

Semantic search extends the scope of conventional information
search and retrieval paradigms from documentoriented
and to entity and knowledge-centric search and retrieval.
By attempting to provide direct and intuitive answers
such systems alleviate information overload problem
and reduce information seekers? cognitive overhead.
Ontologies and knowledge bases are fundamental cornerstones
in semantic search systems based on which sophisticated
search mechanisms and efficient search services
are designed. Nevertheless, acquisition of quality knowledge
from heterogeneous sources on the Web is never a
trivial task. Transformation of data in existing databases
seems a promising bootstrapping approach, while information
providers may refuse to do so because of intellectual
property issues. In this article we discuss issues related to
knowledge acquisition for semantic search systems. In particular,
we discuss ontology learning from unstructured text
corpus, which is an automatic knowledge acquisition process
using different techniques.

This paper describes a framework for perception
creation from sensor data. We propose using data abstraction
techniques, in particular Symbolic Aggregate Approximation
(SAX), to analyse and create patterns from sensor data. The
created patterns are then linked to semantic descriptions that
define thematic, spatial and temporal features, providing highly
granular abstract representation of the raw sensor data. This
helps to reduce the size of the data that needs to be
communicated from the sensor nodes to the gateways or highlevel
processing components. We then discuss a method that uses
abstract patterns created by SAX method and occurrences of
different observations in a knowledge-based model to create
perceptions from sensor data.

With the growing popularity of Information and Communications Technologies (ICT) and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMQP). We provide a comprehensive analysis on the effect of adaptive and non-adaptive window size in segmentation of time series using SensorSAX and SAX approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with 3 parameters, namely window size parameter of the SAX algorithm, sensitivity level and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilised segmentation approach, due to the fact that each geographically different sensory environment has got different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

Automated service discovery enables human users or software agents to form queries and to search and discover the services based on different requirements. This enables implementation of high-level functionalities such as service recommendation, composition, and provisioning. The current service search and discovery on the Web is mainly supported by text and keyword based solutions which offer very limited semantic expressiveness to service developers and consumers. This paper presents a method using probabilistic machine-learning techniques to extract latent factors from semantically enriched service descriptions. The latent factors are used to construct a model to represent different types of service descriptions in a vector form. With this transformation, heterogeneous service descriptions can be represented, discovered, and compared on the same homogeneous plane. The proposed solution is scalable to large service datasets and provides an efficient mechanism that enables publishing and adding new services to the registry and representing them using latent factors after deployment of the system. We have evaluated our solution against logic-based and keyword-based service search and discovery solutions. The results show that the proposed method performs better than other solutions in terms of precision and normalised discounted cumulative gain values.

The search tools and information retrieval systems on the contemporary Web use keywords, lexical analysis, popularity, and statistical methods to find and prioritise relevant data to a specific query. In recent years, Semantic web has introduced new approaches to specify Web data using machine-interpretable structures. This has led to the establishment of new frameworks for search engines and information systems based on discovering complex and meaningful relationships between information resources. In this paper we discuss a semantic supported information search and retrieval system to answer users? information queries. The paper focuses on knowledge discovery aspects of the system and in particular analysis of semantic associations. The information resources are multimedia data, which could be retrieved from heterogeneous resources. The main goal is to provide a hypermedia presentation, which narratively conveys relevant information to the queried term. The structure describes the related entities to the queried topic and a ranking mechanism assigns weights to the entities. The assigned weights express the degree of relevancy of each related entity in the presentation structure.

The emergence of the Internet of Things (IoT) has
led to the production of huge volumes of real-world streaming
data. We need effective techniques to process IoT data streams
and to gain insights and actionable information from realworld
observations and measurements. Most existing approaches
are application or domain dependent. We propose a method
which determines how many different clusters can be found
in a stream based on the data distribution. After selecting the
number of clusters, we use an online clustering mechanism
to cluster the incoming data from the streams. Our approach
remains adaptive to drifts by adjusting itself as the data changes.
We benchmark our approach against state-of-the-art stream
clustering algorithms on data streams with data drift. We show
how our method can be applied in a use case scenario involving
near real-time traffic data. Our results allow to cluster, label and
interpret IoT data streams dynamically according to the data
distribution. This enables to adaptively process large volumes of
dynamic data online based on the current situation. We show
how our method adapts itself to the changes. We demonstrate
how the number of clusters in a real-world data stream can be
determined by analysing the data distributions.

The need for annotating digital image data is recognised in a variety of different medical information systems, covering both professional and educational usage of medical imaging. Due to the high recall and low precision attribute of keyword-based search, multimedia information search and retrieval based on textual descriptions is not always an efficient and sufficient solution, particularly for specific applications such as the medical diagnosis information systems. On the other hand, using image processing techniques to provide search on the content specific data for multimedia information is not a trivial task. In this paper we use the semantic web technologies in medical image search and retrieval process for a medical imaging information system. We employ an ontology-based knowledge representation and semantic annotation for medical image data. The proposed system defines data representation structures which are given well-defined meanings. The meanings are machine-accessible contents which could be interpreted by the software agents to find and retrieve the information based on the standard vocabularies and meaningful relationships between the data items.

The vision of the Internet of Things (IoT) relies on the provisioning of real-world services, which are provided
by smart objects that are directly related to the physical world. A structured, machine-processible approach to provision such
real-world services is needed to make heterogeneous physical objects accessible on a large scale and to integrate them with the
digital world. The incorporation of observation and measurement data obtained from the physical objects with the Web data, using
information processing and knowledge engineering methods, enables the construction of ?intelligent and interconnected things?.
The current research mostly focuses on the communication and networking aspects between the devices that are used for sensing
amd measurement of the real world objects. There is, however, relatively less effort concentrated on creating dynamic infrastructures
to support integration of the data into the Web and provide unified access to such data on service and application levels. This
paper presents a semantic modelling and linked data approach to create an information framework for IoT. The paper describes
a platform to publish instances of the IoT related resources and entities and to link them to existing resources on the Web. The
developed platform supports publication of extensible and interoperable descriptions in the form of linked data.

The Semantic Web is an extension to the current Web in which information is provided in machine-processable format. It allows interoperable data representation and expression of meaningful relationships between the information resources. In other words, it is envisaged with the supremacy of deduction capabilities on the Web, that being one of the limitations of the current Web. In a Semantic Web framework, an ontology provides a knowledge sharing structure. The research on Semantic Web in the past few years has offered an opportunity for conventional information search and retrieval systems to migrate from keyword to semantics-based methods. The fundamental difference is that the Semantic Web is not a Web of interlinked documents; rather, it is a Web of relations between resources denoting real world objects, together with well-defined metadata attached to those resources. In this chapter, we first investigate various approaches towards ontology development, ontology population from heterogeneous data sources, semantic association discovery, semantic association ranking and presentation, and social network analysis, and then we present our methodology for an ontology-based information search and retrieval. In particular, we are interested in developing efficient algorithms to resolve the semantic association discovery and analysis issues.

Research on semantic search aims to improve conventional
information search and retrieval methods, and facilitate
information acquisition, processing, storage and retrieval
on the semantic web. The past ten years have seen a number
of implemented semantic search systems and various proposed
frameworks. A comprehensive survey is needed to gain
an overall view of current research trends in this field. We
have investigated a number of pilot projects and corresponding
practical systems focusing on their objectives, methodologies
and most distinctive characteristics. In this paper, we report
our study and findings based on which a generalised semantic
search framework is formalised. Further, we describe issues
with regards to future research in this area.

In this paper, we study some of the most common formation protocols for scatternets such as BlueTrees, BlueNet, and BlueStars. The paper focuses on security mechanisms that are needed to provide secure communication among the nodes in the scatternet We propose a secure communication between two parties based on encryption mechanisms. In this approach secret keys are proposed for each pair. The focus of the suggested method is the scatternet communication security and in particular the secret key exchange. The paper describes a mechanism for the key agreement procedure through a secure scatternet formation protocol.

This paper focuses on service clustering and uses service descriptions
to construct probabilistic models for service clustering.We discuss
how service descriptions can be enriched with machine-interpretable
semantics and then we investigate how these service descriptions can be
grouped in clusters in order to make discovery, ranking, and recommendation
faster and more effective. We propose using Probabilistic Latent
Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA) (i.e.
two machine learning techniques used in Information Retrieval) to learn
latent factors from the corpus of service descriptions and group services
according to their latent factors. By creating an intermediate layer of
latent factors between the services and their descriptions, the dimensionality
of the model is reduced and services can be searched and linked
together based on probabilistic methods in latent space. The model can
cluster any newly added service with a direct calculation without requiring
to re-calculate the latent variables or re-train the model.

This patent is based on our novel data discovery mechanism for large scale, highly distributed and heterogeneous data networks. Managing Big Data harvested from IoT environments is an example application

Barnaghi P, Wang W, Henson C, Tayolor K(2012)Semantics for the Internet of Things: early progress and back to the future, International Journal on Semantic Web and Information Systems8(1) IGI Global

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people's everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large intercon- nected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.

This paper describes a linked-data platform to publish sen-
sor data and link them to existing resource on the semantic Web. The
linked sensor data platform, called Sense2Web supports
exible and in-
teroperable descriptions and provide association of di erent sensor data
ontologies to resources described on the semantic Web and the Web of
data. The current advancements in (wireless) sensor networks and being
able to manufacture low cost and energy e cient hardware for sensors
has lead to much interest in integrating physical world data into theWeb.
Wireless sensor networks employ various types of hardware and software
components to observe and measure physical phenomena and make the
obtained data available through di erent networking services. Applica-
tions and users are typically interested in querying various events and
requesting measurement and observation data from the physical world.
Using a linked data approach enables data consumers to access sensor
data and query the data and relations to obtain information and/or inte-
grate data from various sources. Global access to sensor data can provide
a wide range of applications in di erent domains such as geographical
information systems, healthcare, smart homes, and business applications
and scenarios. In this paper we focus on publishing linked-data to anno-
tate sensors and link them to other existing resources on the Web.

This paper describes an ontology validation tool that is designed for the W3C Semantic Sensor Networks Ontology (W3C SSN). The tool allows ontologies and linked-data descriptions to be validated against the concepts and properties used in the W3C SSN model. It generates validation reports and collects statistics regarding the most commonly used terms and concepts within the ontologies. An online version of the tool is available at: (http://iot.ee.Surrey.ac.uk/SSNValidation). This tool can be used as a checking and validation service for new ontology developments in the IoT domain. It can also be used to give feedback to W3C SSN and other similar ontology developers regarding the most commonly used concepts and properties from the reference ontology and this information can be used to create core ontologies that have higher level interoperability across different systems and various application domains.

Gateways in sensor networks are used to relay, aggregate and communicate information from capillary networks to more capable (e.g. IP-based) networks. However Gateway-to-Gateway (G2G) communication to exchange and update information among the gateways in large-scale sensor networks for query processing, data fusion and other similar tasks has been less discussed in recent works. The requirements for large-scale sensor networks such as dynamic topology and update strategies to reduce the overall network load makes G2G communications an important aspect in the network design. In this paper, we introduce a mediated gossip-based G2G communication mechanism. The proposed solution leverages the publish/subscribe approach and uses high-level context assigned to publish/subscribe channels to enable the information discovery and G2G communications. Gateways store/aggregate sensor observation and measurement data according to specific context which is defined based on features such as spatial and temporal attributes, observed phenomena (i.e. feature of interest) and sensor device features. The gateways communicate with each other to exchange data and also to forward related queries for data aggregation in cases that the data should be aggregated from two different sources. The proposed solution also facilitates reliable sensor service provisioning by enabling gateways to communicate and/or forward requests to other gateways when a resource fails or a sensor node becomes unavailable. We compare our results to probabilistic gossiping algorithms and run benchmarks on different dynamic network topologies based on indicators such as number of sent messages and dissemination delay.

The advent of Internet of Things, has resulted in the development of infrastructure for capturing and storing data from domains ranging from smart devices (e.g. smartphones) to smart cities. This data is often available publicly and has enabled a wider range of data consumers to utilise such data sets for applications ranging from scientific experimentation to enhancing commercial activity for businesses. Accordingly this has resulted in the need for the development data analysis tools that are both simple to use and provide the most effective tools for a given data set. To this end, we introduce data analysis tools as web service, that enables the data consumer to make a simple HTTP request for processing data over the internet. By providing such tools as a web service, we demonstrate the potential of such a system to aid both the advanced and novice data consumer. Furthermore, this work provides an use case example of the proposed tool on publicly available data extracted from the smart city CityPulse IoT project.

In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.

The W3C Semantic Sensor Network Incubator group (the SSN-XG) produced an OWL 2 ontology to describe sensors and observations ? the SSNontology, available at http://purl.oclc.org/NET/ssnx/ssn. The SSNontology can describe sensors in terms of capabilities, measurement processes, observations and deployments. This article describes the SSNontology. It further gives an example and describes the use of the ontology in recent research projects.

The problem of learning concept hierarchies and terminological ontologies can be divided into two subtasks:
concept extraction and relation learning. The authors of this chapter describe a novel approach
to learn relations automatically from unstructured text corpus based on probabilistic topic models. The
authors provide definition (Information Theory Principle for Concept Relationship) and quantitative
measure for establishing ?broader? (or ?narrower?) and ?related? relations between concepts. They
present a relation learning algorithm to automatically interconnect concepts into concept hierarchies
and terminological ontologies with the probabilistic topic models learned. In this experiment, around
7,000 ontology statements expressed in terms of ?broader? and ?related? relations are generated using
different combination of model parameters. The ontology statements are evaluated by domain experts
and the results show that the highest precision of the learned ontologies is around 86.6% and structures
of learned ontologies remain stable when values of the parameters are changed in the ontology learning
algorithm.

The integration of the physical world into the digital world is an important requirement for a Future Internet, as an increasing number of services and applications are relying on real world information and interaction capabilities. Sensor and actuator networks (SAN) are the current means of interacting with the real world although most of the current deployments represent closed vertically integrated solutions. In this paper we present an architecture that enables efficient integration of these heterogeneous and distributed SAN islands into a homogeneous framework for real world information and interactions, contributing to a horizontal reuse of the deployed infrastructure across a variety of application domains. We present the main concepts, their relationships and the proposed real world resource based architecture. Finally, we outline an initial implementation of the architecture based on the current Internet and web technologies.

This work proposes a pattern identification and
online prediction algorithm for processing Internet of
Things (IoT) time-series data. This is achieved by
first proposing a new data aggregation and datadriven
discretisation method that does not require data
segment normalisation. We apply a dictionary based
algorithm in order to identify patterns of interest along
with prediction of the next pattern. The performance
of the proposed method is evaluated using synthetic
and real-world datasets. The evaluations results shows
that our system is able to identify the patterns by up to
85% accuracy which is 16.5% higher than a baseline
using the Symbolic Aggregation Approximation (SAX)
method.

This paper describes the design and development
of a wireless monitoring system for use within a pediatric
environment. The current wired methods used to provide noninvasive
sensing are not best suited to their end user, and there is
a development need for platform independent data transmission.
The main goal has been to develop a practical and flexible
proof-of-concept prototype suitable for the transmission of sensor
data. This prototype consists of an Arduino based multi-input
sensor system with wireless transmission, and an Android
monitoring station with the facility to rebroadcast the collected
data via email/web as a data file. This was achieved using
commercially available hardware platforms. The software
produced for the Android device allows for full control of the
functionality provided by the sensor platform developed on the
Arduino system, as well as storing the data within a relational
database. The data can also be graphically represented in realtime
on the Android device.

Recent advancements in sensing, networking technologies
and collecting real-world data on a large scale and from various environments
have created an opportunity for new forms of real-world services
and applications. This is known under the umbrella term of the Internet
of Things (IoT). Physical sensor devices constantly produce very large
amounts of data. Methods are needed which give the raw sensor measurements
a meaningful interpretation for building automated decision
support systems. To extract actionable information from real-world data,
we propose a method that uncovers hidden structures and relations
between multiple IoT data streams. Our novel solution uses Latent
Dirichlet Allocation (LDA), a topic extraction method that is generally
used in text analysis. We apply LDA on meaningful abstractions that
describe the numerical data in human understandable terms. We use
Symbolic Aggregate approXimation (SAX) to convert the raw data into
string-based patterns and create higher level abstractions based on
rules.

We finally investigate how heterogeneous sensory data from multiple
sources can be processed and analysed to create near real-time intelligence
and how our proposed method provides an efficient way to
interpret patterns in the data streams. The proposed method uncovers
the correlations and associations between different pattern in IoT data
streams. The evaluation results show that the proposed solution is able
to identify the correlation with high efficiency with an F-measure up to
90%.

this paper presents a novel approach in targeting load balancing in ad hoc networks utilizing the properties of quantum game theory. This approach benefits from the instantaneous and information-less capability of entangled particles to synchronize the load balancing strategies in ad hoc networks. The Quantum Load Balancing (QLB) algorithm proposed by this work is implemented on top of OLSR as the baseline routing protocol; its performance is analyzed against the baseline OLSR, and considerable gain is reported regarding some of the main QoS metrics such as delay and jitter. Furthermore, it is shown that QLB algorithm supports a solid stability gain in terms of throughput which stands a proof of concept for the load-balancing properties of the proposed theory.

Over the past few years the semantics community
has developed ontologies to describe concepts and relationships
between different entities in various application domains,
including Internet of Things (IoT) applications. A key problem
is that most of the IoT related semantic descriptions are not
as widely adopted as expected. One of the main concerns
of users and developers is that semantic techniques increase
the complexity and processing time and therefore they are
unsuitable for dynamic and responsive environments such as
the IoT. To address this concern, we propose IoT-Lite, an
instantiation of the semantic sensor network (SSN) ontology
to describe key IoT concepts allowing interoperability and
discovery of sensory data in heterogeneous IoT platforms by
a lightweight semantics. We propose 10 rules for good and
scalable semantic model design and follow them to create
IoT-Lite. We also demonstrate the scalability of IoT-Lite by
providing some experimental analysis, and assess IoT-Lite
against another solution in terms of round time trip (RTT)
performance for query-response times.

Over the past few years the semantics community has developed several ontologies to describe concepts and relationships for Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time and therefore they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis, and assess IoT-Lite against another solution in terms of round trip time (RTT) performance for query-response times. We have linked IoTLite with Stream Annotation Ontology (SAO), to allow queries over stream data annotations and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

The Internet of Things (IoT) has become a new enabler for collecting real-world observation and measurement data from the physical world. The IoT allows objects with sensing and network capabilities (i.e. Things and devices) to communicate with one another and with other resources (e.g. services) on the digital world. The heterogeneity, dynamicity and ad-hoc nature of underlying data, and services published by most of IoT resources make accessing and processing the data and services a challenging task. The IoT demands distributed, scalable, and efficient indexing solutions for large-scale distributed IoT networks. We describe a novel distributed indexing approach for IoT resources and their published data. The index structure is constructed by encoding the locations of IoT resources into geohashes and then building a quadtree on the minimum bounding box of the geohash representations. This allows to aggregate resources with similar geohashes and reduce the size of the index. We have evaluated our proposed solution on a large-scale dataset and our results show that the proposed approach can efficiently index and enable discovery of the IoT resources with 65% better response time than a centralised approach and with a high success rate (around 90% in the first few attempts).

In this paper we discuss a technical design and an
ongoing trial that is being conducted in the UK, called Technology
Integrated Health Management (TIHM). TIHM uses Internet of
Things (IoT) enabled solutions provided by various companies
in a collaborative project. The IoT devices and solutions are
integrated in a common platform that supports interoperable
and open standards. A set of machine learning and data analytics
algorithms generate notifications regarding the well-being of the
patients. The information is monitored around the clock by a
group of healthcare practitioners who take appropriate decisions
according to the collected data and generated notifications. In
this paper we discuss the design principles and the lessons that
we have learned by co-designing this system with patients, their
carers, clinicians, and also our industry partners. We discuss
the technical design of TIHM and explain why user-centred and
human-experience should be an integral part of the technological
design.

Risk profiling of oncology patients based on their symptom experience assists
clinicians to provide more personalized symptom management interventions. Recent findings
suggest that oncology patients with distinct symptom profiles can be identified using a variety of
analytic methods.

Objectives:

To evaluate the concordance between the number and types of subgroups of
patients with distinct symptom profiles using latent class analysis (LCA) and K-modes analysis.

Methods:

Using data on the occurrence of 25 symptoms from the Memorial Symptom
Assessment Scale (MSAS), that 1329 patients completed prior to their next dose of
chemotherapy (CTX), Cohen?s kappa coefficient was used to evaluate for concordance between
the two analytic methods. For both LCA and K-modes, differences among the subgroups in
demographic, clinical, and symptom characteristics, as well as quality of life outcomes were
determined using parametric and nonparametric statistics.

Results:

Using both analytic methods, four subgroups of patients with distinct symptom profiles
were identified (i.e., All Low, Moderate Physical and Lower Psychological, Moderate Physical
and Higher Psychological, All High). The percent agreement between the two methods was
75.32% which suggests a moderate level of agreement. In both analyses, patients in the All
High group were significantly younger and had a higher comorbidity profile, worse MSAS
subscale scores, and poorer QOL outcomes.

Conclusion:

Both analytic methods can be used to identify subgroups of oncology patients with
distinct symptom profiles. Additional research is needed to determine which analytic methods
and which dimension of the symptom experience provides the most sensitive and specific risk
profiles.

An important field in exploratory sensory data
analysis is the segmentation of time-series data to identify
activities of interest. In this work, we analyse the performance
of univariate and multi-sensor Bayesian change detection
algorithms in segmenting accelerometer data. In particular, we
provide theoretical analysis and also performance evaluation on
synthetic data and real-world data. The results illustrate the
advantages of using multi-sensory variance change detection in
the segmentation of dynamic data (e.g. accelerometer data).

The current Web and data indexing and search mechanisms are mainly tailored to process text-based data and are limited in addressing the intrinsic characteristics of distributed, large-scale and dynamic Internet of Things (IoT) data networks. The IoT demands novel indexing solutions for large-scale data to create an ecosystem of system; however, IoT data are often numerical, multi-modal and heterogeneous. We propose a distributed and adaptable mechanism that allows indexing and discovery of real-world data in IoT networks. Comparing to the state-of-the-art approaches, our model does not require any prior knowledge about the data or their distributions. We address the problem of distributed, efficient indexing and discovery for voluminous IoT data by applying an unsupervised machine learning algorithm. The proposed solution aggregates and distributes the indexes in hierarchical networks. We have evaluated our distributed solution on a large-scale dataset, and the results show that our proposed indexing scheme is able to efficiently index and enable discovery of the IoT data with 71% to 92% better response time than a centralised approach.

Machine learning has been used to accurately recognise physical activity patterns; however, classifiers for recognising targeted bone loading exercises have not been developed.

PURPOSE:

The purpose of this study was to determine the accuracy of machine learning models for classifying the intensity of exercises necessary for bone adaption in older adults.

METHODS:

Triaxial accelerometer data was collected from forty-four older participants (60-70 yrs) wearing a GCDC X16-1C accelerometer on their hip during three aerobics classes consisting of impact aerobic exercises performed at high and low intensities. Multi-class support vector machine (M-SVM) classifiers were trained in parallel for activity type detections where one classifier trained with low intensity activity samples and the other with high intensity samples. In a multi-view scoring manner, the classification confidence of these two learners was utilised for predicting the activity intensity. The leave-one-out cross-validation technique was used for assessment purpose.

RESULTS:

Overall recognition accuracy of the M-SVM classifier for detecting exercise intensity was 73%. For each aerobics class, the M-SVM classifier accurately recognised exercise intensity by 82%, 73% and 65%.

CONCLUSIONS:

Machine learning techniques such as M-SVM accurately recognised the intensity of bone promoting exercises from triaxial accelerometer data in community-dwelling older adults. First results of the developed classifier demonstrate significant potential of machine learning models for the evaluation of exercise adherence and performance in older adults.

Risk profiling of oncology patients based on their symptom experience assists clinicians to provide more personalized symptom management interventions. Recent findings suggest that oncology patients with distinct symptom profiles can be identified using a variety of analytic methods.

Objectives

To evaluate the concordance between the number and types of subgroups of patients with distinct symptom profiles using latent class analysis (LCA) and K-modes analysis.

Methods

Using data on the occurrence of 25 symptoms from the Memorial Symptom Assessment Scale (MSAS), that 1329 patients completed prior to their next dose of chemotherapy (CTX), Cohen?s kappa coefficient was used to evaluate for concordance between the two analytic methods. For both LCA and K-modes, differences among the subgroups in demographic, clinical, and symptom characteristics, as well as quality of life outcomes were determined using parametric and nonparametric statistics.

Results

Using both analytic methods, four subgroups of patients with distinct symptom profiles were identified (i.e., All Low, Moderate Physical and Lower Psychological, Moderate Physical and Higher Psychological, All High). The percent agreement between the two methods was 75.32% which suggests a moderate level of agreement. In both analyses, patients in the All High group were significantly younger and had a higher comorbidity profile, worse MSAS subscale scores, and poorer QOL outcomes.

Conclusion

Both analytic methods can be used to identify subgroups of oncology patients with distinct symptom profiles. Additional research is needed to determine which analytic methods and which dimension of the symptom experience provides the most sensitive and specific risk profiles.

Rapid developments in hardware, software, and communication technologies
have allowed the emergence of Internet-connected sensory devices that provide
observation and data measurement from the physical world. By 2020, it is
estimated that the total number of Internet-connected devices being used will
be between 25-50 billion. As the numbers grow and technologies become more
mature, the volume of data published will increase. Internet-connected devices
technology, referred to as Internet of Things (IoT), continues to extend the
current Internet by providing connectivity and interaction between the physical
and cyber worlds. In addition to increased volume, the IoT generates Big Data
characterized by velocity in terms of time and location dependency, with a
variety of multiple modalities and varying data quality. Intelligent processing
and analysis of this Big Data is the key to developing smart IoT applications.
This article assesses the different machine learning methods that deal with the
challenges in IoT data by considering smart cities as the main use case. The
key contribution of this study is presentation of a taxonomy of machine learning
algorithms explaining how different techniques are applied to the data in order
to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying
Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented
for a more detailed exploration.

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber
world. The Internet of Things (IoT) consists of the network-enabled devices and communication technologies
that allow connectivity and integration of physical objects (Things) into the digital world (Internet). Enormous
amounts of dynamic IoT data are collected from Internet-connected devices. IoT data is usually multi-variant
streams that are heterogeneous, sporadic, multi-modal and spatio-temporal. IoT data can be disseminated
with different granularities and have diverse structures, types and qualities. Dealing with the data deluge
from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking
mechanisms that will allow building applications that require on-line access and retrieval of ad-hoc IoT data.
However, the existing IoT data indexing and discovery approaches are complex or centralised which hinders
their scalability. The primary objective of this paper is to provide a holistic overview of the state-of-the-art on
indexing, discovery and ranking of IoT data. The paper aims to pave the way for researchers to design, develop,
implement and evaluate techniques and approaches for on-line large-scale distributed IoT applications and
services.

Smart Cities use different Internet of Things (IoT) data sources and rely on big data analytics to obtain information or extract actionable knowledge crucial for urban planners for efficiently use and plan the construction infrastructures. Big data analytics algorithms often consider the correlation of different patterns and various data types. However, the use of different techniques to measure the correlation with smart cities data and the exploitation of correlations to infer new knowledge are still open questions. This paper proposes a methodology to analyse data streams, based on spatio-temporal correlations using different correlation algorithms and provides a discussion on co-occurrence vs. causation. The proposed method is evaluated using traffic data collected from the road sensors in the city of Aarhus in Denmark.

This work addresses the problem of segmentation in time series
data with respect to a statistical parameter of interest in
Bayesian models. It is common to assume that the parameters
are distinct within each segment. As such, many Bayesian
change point detection models do not exploit the segment parameter
patterns, which can improve performance. This work
proposes a Bayesian mean-shift change point detection algorithm
that makes use of repetition in segment parameters, by
introducing segment class labels that utilise a Dirichlet process
prior. The performance of the proposed approach was
assessed on both synthetic and real world data, highlighting
the enhanced performance when using parameter labelling.

Enormous amounts of dynamic observation and
measurement data are collected from sensors in Wireless
Sensor Networks (WSNs) for the Internet of Things (IoT)
applications such as environmental monitoring. However, continuous
transmission of the sensed data requires high energy
consumption. Data transmission between sensor nodes and
cluster heads (sink nodes) consumes much higher energy than
data sensing in WSNs. One way of reducing such energy
consumption is to minimise the number of data transmissions.
In this paper, we propose an Adaptive Method for Data Reduction
(AM-DR). Our method is based on a convex combination
of two decoupled Least-Mean-Square (LMS) windowed filters
with differing sizes for estimating the next measured values
both at the source and the sink node such that sensor nodes
have to transmit only their immediate sensed values that
deviate significantly (with a pre-defined threshold) from the
predicted values. The conducted experiments on a real-world
data show that our approach has been able to achieve up to
95% communication reduction while retaining a high accuracy
(i.e. predicted values have a deviation of ý+0:5 from real data
values).

Data discovery for sensor data in an M2M network uses probabilistic models, such as Gaussian Mixing Models (GMMs) to represent attributes of the sensor data. The parameters of the probabilistic models can be provided to a discovery server (DS) that respond to queries concerning the sensor data. Since the parameters are compressed compared to the attributes of the sensor data itself, this can simplify the distribution of discovery data. A hierarchical arrangement of discovery servers can also be used with multiple levels of discovery servers where higher level discovery servers using more generic probabilistic models.

The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to
reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and
represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced
mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes
the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting
matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is
designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining
real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation
(SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS
is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of
the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

The data gathered from smart cities can help citizens and city manager
planners know where and when they should be aware of the
repercussions regarding events happening in different parts of the
city. Most of the smart city data analysis solutions are focused on
the events and occurrences of the city as a whole, making it difficult
to discern the exact place and time of the consequences of a particular
event. We propose a novel method to model the events in a city
in space and time. We apply our methodology for vehicular traffic
data basing our models in (convolutional) neuronal networks.

When dealing with a large number of devices, the existing indexing solutions for the discovery of IoT sources often fall short
to provide an adequate scalability. This is due to the high computational complexity and communication overhead that is required to
create and maintain the indices of the IoT sources particularly when their attributes are dynamic. This paper presents a novel approach
for indexing distributed IoT sources and paves the way to design a data discovery service to search and gain access to their data. The
proposed method creates concise references to IoT sources by using Gaussian Mixture Models (GMM). Furthermore, a summary update
mechanism is introduced to tackle the change of sources availability and mitigate the overhead of updating the indices frequently. The
proposed approach is benchmarked against a standard centralized indexing and discovery solution. The results show that the proposed
solution reduces the communication overhead required for indexing by three orders of magnitude while depending on IoT network
architecture it may slightly increase the discovery time

The Internet of Things (IoT) offers an incredible
innovation potential for developing smarter applications and
services. However, today we see solutions in the development of
vertical applications and services reflecting what used to be the
early days of the Web, leading to fragmentation and intra-nets of
Things. To achieve an open IoT ecosystem of systems and
platforms, several key enablers are needed for effective, adaptive
and scalable mechanisms for exploring and discovering IoT
resources and their data/capabilities. This paper discusses our
work in the EU H2020 IoTCrawler project. Its focus is on the
integration and interoperability across different platforms,
through dynamic and reconfigurable solutions for discovery and
integration of data and services from legacy and new systems. This
is complemented with adaptive, privacy-aware and secure
solutions for crawling, indexing and searching in distributed IoT
systems. IoTCrawler targets IoT development and demonstrations
with a focus on Industry 4.0, Social IoT, Smart City and Smart
Energy use cases.

In pervasive environments, availability and reliability of a service cannot always be guaranteed. In such environments, automatic and dynamic mechanisms are required to compose services or compensate for a service that becomes unavailable during the runtime. Most of the existing works on services composition do not provide sufficient support for automatic service provisioning in pervasive environments. We propose a Divide and Conquer algorithm that can be used at the service runtime to repeatedly divide a service composition request into several simpler sub-requests. The algorithm repeats until for each sub-request we find at least one atomic service that meets the requirements of that sub-request. The identified atomic services can then be used to create a composite service. We discuss the technical details of our approach and show evaluation results based on a set of composite service requests. The results show that our proposed method performs effectively in decomposing a composite service requests to a number of sub-requests and finding and matching service components that can fulfill the service composition request.

Recent advancements in sensing, networking technologies and collecting real-world data on a large scale and from various environments
have created an opportunity for new forms of services and applications. This is known under the umbrella term of the Internet of
Things (IoT). Physical sensor devices constantly produce very large amounts of data. Methods are needed which give the raw sensor measurements a meaningful interpretation for building automated decision support systems. One of the main research challenges in this domain is to extract actionable information from real-world data, that is information that can readily be used to make informed automatic
decisions in intelligent systems. Most existing approaches are application or domain dependent or are only able to deal with specific data
sources of one kind. This PhD research concerns multiple approaches for analysing IoT data streams. We propose a method which determines how many different clusters can be found in a stream based on the data distribution. After selecting the number of clusters, we use an online clustering mechanism to cluster the incoming data from the streams. Our approach remains adaptive to drifts by adjusting itself as the data changes. The work is benchmarked against state-of-the art stream clustering algorithms on data streams with data drift. We show how our method can be applied in a use case scenario involving near real-time traffic data. Our results allow to cluster, label and interpret IoT data streams dynamically according to the data distribution. This enables to adaptively process large volumes of dynamic data online based on the current situation. We show how our method adapts itself to the changes and we demonstrate how the number of clusters in a real-world data stream can be determined by analysing the data distributions.
Using the ideas and concepts of this approach as a starting point we designed another novel dynamic and adaptable clustering approach
that is more suitable for multi-variate time-series data clustering. Our solution uses probability distributions and analytical methods to adjust the centroids as the data and feature distributions change over time. We have evaluated our work against some well-known time-series clustering methods and have shown how the proposed method can reduce the complexity and perform efficient in multi-variate datastreams.
Finally we propose a method that uncovers hidden structures and relations between multiple IoT data streams. Our novel solution uses Latent Dirichlet Allocation (LDA), a topic extraction method that is generally used in text analysis. We apply LDA on meaningful labels that describe the numerical data in human understandable terms. To create the labels we use Symbolic Aggregate approXimation (SAX), a method that converts raw data into string-based patterns. The extracted patterns are then transformed with a rule engine into the labels.
The work investigates how heterogeneous sensory data from multiple sources can be processed and analysed to create near real-time intelligence and how our proposed method provides an efficient way to interpret patterns in the data streams. The proposed method provides a novel way to uncover the correlations and associations between different pattern in IoT data streams. The evaluation results show that the proposed solution is able to identify the correlation with high efficiency with an F-measure up to 90%.
Overall, this PhD research has designed, implemented and evaluated unsupervised adaptive algorithms to analyse, structure and extract information from dynamic and multi-variate sensory data streams. The results of this research has significant impact in designing flexible and scalable solutions in analysing real-world sensory data streams and specially in cases where labelled and annotated data is not available or it is too costly to be collected. Research and advancements in healthcare and smarter cities are two key areas that can directly fr

An increasing number of cities are confronted with challenges resulting from the rapid urbanisation and new demands that
a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city
authorities to monitor, manage and provide plans for public resources and infrastructures in city environments, while offering citizens
and businesses to develop and use intelligent services in cities. However, providing such smart city applications gives rise to several
issues such as semantic heterogeneity and trustworthiness of data sources, and extracting up-to-date information in real time from
large-scale dynamic data streams. In order to address these issues, we propose a novel framework with an efficient semantic data
processing pipeline, allowing for real-time observation of the pulse of a city. The proposed framework enables efficient semantic
integration of data streams and complex event processing on top of real-time data aggregation and quality analysis in a Semantic Web
environment. To evaluate our system, we use real-time sensor observations that have been published via an open platform called Open
Data Aarhus by the City of Aarhus. We examine the framework utilising Symbolic Aggregate Approximation to reduce the size of data
streams, and perform quality analysis taking into account both single and multiple data streams. We also investigate the optimisation of
the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.

Network-enabled sensing and actuation devices are key enablers to connect real-world objects to the cyber world. The Internet of Things (IoT) consists of network-enabled devices and communication technologies that allow connectivity and integration of physical objects (Things) into the digital world (Internet).

Dealing with the data deluge from heterogeneous IoT resources and services imposes new challenges on indexing, discovery and ranking mechanisms. Novel indexing and discovery methods will enable developing applications that use on-line access and retrieval of ad-hoc IoT data.

Investigation of the related work leads to the conclusion that there has been significant work on processing and analysing sensor data streams. However, there is still a need for integrating solutions that contemplate the work-flow from connecting IoT resources to make their published data indexable, searchable and discoverable.

This research proposes a set of novel solutions for indexing, processing and discovery in IoT networks. The work proposes novel distributed in-network and spatial indexing solutions. The proposed solutions scale well and provide up to 92% better response time and higher success rates in response to data search queries compared to a baseline approach.

A co-operative, adaptive, change detection algorithm has also been developed. It is based on a convex combination of two decoupled Least Mean Square (LMS) windowed filters. The approach provides better performance and less complexity compared to the state-of-the-art solutions. The change detection algorithm can also be applied to distributed networks in an on-line fashion. This co-operative approach allows publish/subscribe based and change based discovery solutions in IoT.

Continuous transmission of large volumes of data collected by sensor nodes induces a high communication cost
for each individual node in IoT networks. An Adaptive Method for Data Reduction (AM-DR) has been proposed for reducing the number of data transmissions in IoT networks. In AM-DR, identical predictive models are constructed at both the sensor and the sink nodes to describe data evolution such that sensor nodes require transmitting only their readings that deviate significantly from actual values. This has a significant impact on reducing the data load in IoT data discovery scenarios.

Finally, a solution for quality and energy-aware resource discovery and accessing IoT resources has been proposed. The solution effectively achieves a communication reduction while retaining a high prediction accuracy (i.e. only a deviation of ±1.0 degree between actual and predicted sensor readings). Furthermore, an energy cost model has been discussed to demonstrate how the proposed approach reduces energy consumption significantly and effectively prolongs the network lifetime.

There has been a keen interest in detecting abrupt
sequential changes in streaming data obtained from sensors
in Wireless Sensor Networks (WSNs) for Internet of Things
(IoT) applications such as fire/fault detection, activity recognition
and environmental monitoring. Such applications require (near)
online detection of instantaneous changes. This paper proposes
an Online, adaptive Filtering-based Change Detection (OFCD)
algorithm. Our method is based on a convex combination of
two decoupled Least Mean Square (LMS) windowed filters with
differing sizes. Both filters are applied independently on data
streams obtained from sensor nodes such that their convex combination
parameter is employed as an indicator of abrupt changes
in mean values. An extension of our method (OFCD) based
on a Cooperative scheme between multiple sensors (COFCD) is
also presented. It provides an enhancement of both convergence
and steady-state accuracy of the convex weight parameter. Our
conducted experiments show that our approach can be applied in
distributed networks in an online fashion. It also provides better
performance and less complexity compared with the state-of-theart
on both of single and multiple sensors.

Pioneering advances have been made in Internet of Things technologies (IoT) in healthcare. This article describes the development and testing of a bespoke IoT system for dementia care. TIHM for dementia is part of the NHS England National Test Bed Programme and has been trailing the deployment of network enabled devices combined with artificial intelligence to improve outcomes for people with dementia and their carers. TIHM uses machine learning and complex algorithms to detect and predict early signs of ill health. The premise is if changes in a person?s health or routine can be identified early on, support can be targeted at the point of need to prevent the development of more serious complications.

The Internet of things (IoT) refers to uniquely identifiable objects and their virtual representations
in an Internet-like structure to be managed and inventoried by computers. Radio-frequency
identification (RFID) - a prerequisite for the IoT - is an automatic way for data transaction in
object identification and is used to improve automation, inventory control and checkout
operations. An RFID system consists of a reader device and one or several tags. Smart reader
systems are building blocks for cutting edge applications of RFID and as a subdivision of these
systems, RFID smart shelf solutions are started to be implemented for large-scale item-level
management where characteristics of reader antennas are critical issue.

This work focuses on designing optimised reader antennas for high frequency (HF) RFID
smart shelf systems which operate based on inductive coupling between the tag and the reader
antennas and have good performance in crowded environments. Firstly, an approach is presented
to increase band-width of HF RFID reader antennas to improve the reception of sub-carrier
frequencies. A fabricated enhanced band-width antenna at 13.56 MHz is evaluated for its
capability in being used for smart shelf applications. The obtained band-width supports sub-carrier
frequencies for all the HF RFID standards to be detected easier and thus leads to increased
identification range. It is shown the HF RFID technology is capable of identifying the distance of
tagged books based on the received magnetic field intensity.

Secondly, multi turn small self resonant coil (MT SSRC) antennas are introduced and analysed
as a new model of inductively coupled reader antennas. Based on the analysis, two turn planar
SSRC (TTP SSRC) antennas having similar dimension with the current HF RFID reader antennas
are investigated. Fabricated TTP SSRC antenna operating at 13.56 MHz is resulted to optimised Q
factor and more uniform near field pattern in comparison with the similar antennas. Also, a
number of TTP SSRC antennas operating at a distinct frequency, 13.56MHz, are fabricated on
different substrates and it is shown the desired Q factor and antenna dimension can be obtained
based on the dielectric characteristics of the substrate.

Event detection has been studied and researched for many years and it has been applied in real world applications with the aim of characterising a situation in the real world. In order to capture a situation, Wireless Sensor Networks (WSNs) are deployed and sensor nodes are used to sense the entities of interest for the real world application; sensing the environment results in the production of a large and often continuous production of raw data. In this context, event detection is used in order to extract the most relevant and useful information from this large set of data. The constraints of nodes have to be taken into account such as energy, computation, and memory.
The environment is observed from a program that is hosted on a sensor node. Machine learning and data mining techniques are embedded in the program to learn from the environment and detect events. A collaborative sensing is a technology to process an event from distrusted nodes which can enhance an accuracy result that can be fault or event.
This research studied processing sensor data to detect events using multiple sensor nodes. A model and/or rules are defined in order to detect an outlier from data matching between sensor data and the model and/or rules. An outlier is analysed and processed to detect an event.
The main contributions of this work have been on collaborative sensing in different sensors including clustering analysis for data labelling, classification analysis in order to process an outlier for an event detection.

Oncology patients undergoing cancer treatment experience an average of fifteen unrelieved symptoms that are highly variable
in both their severity and distress. Recent advances in Network Analysis (NA) provide a novel approach to gain insights into
the complex nature of co-occurring symptoms and symptom clusters and identify core symptoms. We present findings from the
first study that used NA to examine the relationships among 38 common symptoms in a large sample of oncology patients
undergoing chemotherapy. Using two different models of Pairwise Markov Random Fields (PMRF), we examined the nature
and structure of interactions for three different dimensions of patients? symptom experience (i.e., occurrence, severity, distress).
Findings from this study provide the first direct evidence that the connections between and among symptoms differ depending
on the symptom dimension used to create the network. Based on an evaluation of the centrality indices, nausea appears
to be a structurally important node in all three networks. Our findings can be used to guide the development of symptom
management interventions based on the identification of core symptoms and symptom clusters within a network.

Effective symptom management is a critical component of cancer treatment.
Computational tools that predict the course and severity of these symptoms have the
potential to assist oncology clinicians to personalize the patient's treatment regimen
more efficiently and provide more aggressive and timely interventions. Three common
and inter-related symptoms in cancer patients are depression, anxiety, and sleep
disturbance. In this paper, we elaborate on the efficiency of Support Vector Regression
(SVR) and Non-linear Canonical Correlation Analysis by Neural Networks (n-CCA) to
predict the severity of the aforementioned symptoms between two different time points
during a cycle of chemotherapy (CTX). Our results demonstrate that these two
methods produced equivalent results for all three symptoms. These types of predictive
models can be used to identify high risk patients, educate patients about their symptom
experience, and improve the timing of pre-emptive and personalized symptom
management interventions.

Dementia is a neurological and cognitive condition that aýects millions of people around
the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied
by a person with dementia, while about 22% of these hospital admissions are due to
preventable causes. In this paper we discuss using Internet of Things (IoT) technologies
and in-home sensory devices in combination with machine learning techniques to
monitor health and well-being of people with dementia. This will allow us to provide
more eýective and preventative care and reduce preventable hospital admissions.
One of the unique aspects of this work is combining environmental data with
physiological data collected via low cost in-home sensory devices to extract actionable
information regarding the health and well-being of people with dementia in their own
home environment. We have worked with clinicians to design our machine learning
algorithms where we focused on developing solutions for real-world settings. In our
solutions, we avoid generating too many alerts/alarms to prevent increasing the
monitoring and support workload. We have designed an algorithm to detect Urinary
Tract Infections (UTI) which is one of the top ýve reasons of hospital admissions for
people with dementia (around 9% of hospital admissions for people with dementia in
the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix
Factorisation (NMF) technique to extract latent factors from raw observation and use
them for clustering and identifying the possible UTI cases. In addition, we have
designed an algorithm for detecting changes in activity patterns to identify early
symptoms of cognitive decline or health decline in order to provide personalised and
preventative care services. For this purpose, we have used an Isolation Forest (iForest)
technique to create a holistic view of the daily activity patterns. This paper describes
the algorithms and discusses the evaluation of the work using a large set of real-world
data collected from a trial with people with dementia and their caregivers.

Edge computing can improve the scalability and
efficiency of IoT systems by performing some of the analysis
and operations on the nodes or on intermediary edge devices.
This will reduce the energy consumption, data transmission
load and latency by shifting some of the processes to the edge
devices. In this paper, we introduce a pattern extraction method
which uses both the Lagrangian Multiplier and the Principal
Component Analysis (PCA) to create patterns from raw sensory
data. We have evaluated our method by applying a clustering
method on constructed patterns. The results show that by using
our proposed Lagrangian-based pattern extraction method, the
existing clustering algorithms perform more accurately - by
up to 20% higher compared with the state-of-the-art methods,
especially in dealing with dynamic real-world data. We have
conducted our evaluations based on synthetic and real-world data
sets and have compared the results to the existing state-of-the-art
approaches. We also discuss how the proposed methods can be
embedded into the edge computing devices in IoT systems and
applications.

In recent years, the development and deployment
of Internet of Things (IoT) devices has led to the generation of
large volumes of real world data. Analytical models can be used to
extract meaningful insights from this data. However, most of IoT
data is not fully utilised, which is mainly due to interoperability
issues and the difficulties to analyse data collected by heterogeneous
resources. To overcome this heterogeneity, semantic
technologies are used to create common models to share various
data originated from heterogeneous sources. However, semantics
add further overhead to data delivery, and the processing time
to annotate the data with the model can increase the latency and
complexity in publishing and querying the annotated data. In
this paper, we present a lightweight semantic model to annotate
IoT streams. The metadata descriptions that are provided in the
models are used for search and discovery of the data using various
attributes such as value and type. The proposed model extends
commonly used ontologies such as W3C/OGC SSN ontology
and its recent lightweight core, SOSA, and includes concepts
to describe streaming IoT data. We also show use cases, tools
and applications where the proposed model has been used.